NMDS for Top 50 abundant species

Set up data for NMDS

split community matrix into two dataframes- one for grouping variables and one for species biomass

#set up final grouping data into dataframe
ME_group_data<-trawl_data_arrange[, c(1,2,3,55,56,57,58)]
ME_NMDS_data<-as.matrix(trawl_data_arrange[,4:53])

Run the NMDS and extract scores

  • change in community composition
  • uses rank order
  • stress < 0.2 is good, < 0.1 is great, <0.05 is excellent representation in reduced dimensions
ME_NMDS=metaMDS(ME_NMDS_data, # Our community-by-species matrix
                k=2, # The number of reduced dimensions
                trymax=200) #increase iterations
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2370547 
## Run 1 stress 0.237059 
## ... Procrustes: rmse 0.0007026836  max resid 0.006237308 
## ... Similar to previous best
## Run 2 stress 0.2374854 
## ... Procrustes: rmse 0.01294161  max resid 0.176785 
## Run 3 stress 0.2378877 
## Run 4 stress 0.2379746 
## Run 5 stress 0.2370549 
## ... Procrustes: rmse 0.0004272761  max resid 0.004227707 
## ... Similar to previous best
## Run 6 stress 0.2370988 
## ... Procrustes: rmse 0.01530707  max resid 0.1770138 
## Run 7 stress 0.2378253 
## Run 8 stress 0.4164693 
## Run 9 stress 0.237887 
## Run 10 stress 0.237097 
## ... Procrustes: rmse 0.01542374  max resid 0.177196 
## Run 11 stress 0.2378878 
## Run 12 stress 0.2371161 
## ... Procrustes: rmse 0.006633357  max resid 0.08890372 
## Run 13 stress 0.2371108 
## ... Procrustes: rmse 0.006674069  max resid 0.08894954 
## Run 14 stress 0.2377685 
## Run 15 stress 0.2370549 
## ... Procrustes: rmse 8.98838e-05  max resid 0.0008687876 
## ... Similar to previous best
## Run 16 stress 0.2374778 
## ... Procrustes: rmse 0.01322304  max resid 0.1769079 
## Run 17 stress 0.2374838 
## ... Procrustes: rmse 0.012942  max resid 0.1767773 
## Run 18 stress 0.2443271 
## Run 19 stress 0.2371161 
## ... Procrustes: rmse 0.006631912  max resid 0.08890015 
## Run 20 stress 0.2370549 
## ... Procrustes: rmse 0.0004285777  max resid 0.004242401 
## ... Similar to previous best
## *** Solution reached
#extract NMDS scores for ggplot
data.scores = as.data.frame(scores(ME_NMDS))
#add columns to data frame 
data.scores$Stratum = trawl_data_arrange$Stratum
data.scores$Region = trawl_data_arrange$Region
data.scores$Year = trawl_data_arrange$Year
data.scores$Season= trawl_data_arrange$Season
data.scores$Year_groups= trawl_data_arrange$YEAR_GROUPS
data.scores$Year_decades= trawl_data_arrange$YEAR_DECADES
data.scores$Region_new=trawl_data_arrange$REGION_NEW
data.scores$Region_year=trawl_data_arrange$REGION_YEAR
data.scores$Season_year=trawl_data_arrange$SEASON_YEAR

Plots

Region

Time

Season